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[en] In this work, we modeled the problem of detection of fruit and leaves in viticulture for proximal applications as a supervised machine learning task. We created and manually labeled a database of images obtained in April 2017 at Guaspari Winery. In total, the database consists of 11,883 images of bunch of grapes and leaves. We trained a convolutional network with YOLOv2 architecture to locate and classify bunch of grapes and leaves. Quantitative tests have shown results for detection and classification with precision of 100%, recall of 74.2% and F1-Score up to 85.2% for the class “grape” and precision of 100%, recall of 67.9% and F1-Score up to 80.9% for the class “leaf”. Also, q ​ ualitative tests show that the model generalizes well when tested on photographs of other grape varieties. ​ These results are promising and are moving towards the possibility of application in the field.